Examining the structure of spatial health effects in Germany using Hierarchical Bayes Models

This paper uses Hierarchical Bayes Models to model and estimate spatial health effects in Germany. We combine rich individual-level household panel data from the German SOEP with administrative county-level data to estimate spatial county-level health dependencies. As dependent variable we use the generic, continuous, and quasi-objective SF12 health measure. We find strong and highly significant spatial dependencies and clusters. The strong and systematic county-level impact is equivalent to 0.35 standard deviations in health. Even 20years after German reunification, we detect a clear spatial East–West health pattern that equals an age impact on health of up to 5 life years for a 40-year old.